Project Structure

Data models must employ certain structural requirements in XINA to ensure they are interpretted correctly by model API calls and front end tools. These apply to both structures within model groups, as well as the organization of model groups themselves.

Projects / Categories

A project should be defined by a single XINA group at the top level. Each model is then defined by a single XINA group, which contain all groups and databases associated exclusively with the model. These should either be defined in the project group, or may be subdivided into category groups.

A project may use a mix of both approaches or additional levels of subcategories if required, but it is recommended to either use a flat structure or single level of category groups to avoid confusion. Models may be referred to by the path relative to their project group (in the above example, model_a would be referenced as model_a or category_a.model_a, respectively).

Project and category groups may also include additional groups and databases of data or resources which are not model specific, such as journals or definitions databases. In most cases with standard structures, models will default to databases or groups within the model, but search for them up the tree if not found. A complete project group might look like:

Project Configuration

A group is defined as a project by the xs_model_project key. The value is a JSON object with the following definition:

Key	Value	Default
def_mn	relative path to mnemonic definitions database	def.mn
def_prof	path to profile definitions database	def.prof
def_plot	path to plot definitions database	def.plot

A group is defined as a category by the xs_model_category key. The value is a JSON object extending the definition of the xs_model_project key, automatically inheriting any unset values from the project configuration.

All models are required to provide an mn_def, prof_def, and plot_def database. It is strongly recommended that these be shared by the entire project, and that all models use the same temporal precision, to maximize intercompatibility between models.

Model Organization

Data within a model falls into four primary classifications:

• Telemetry
  • source data file(s) from data collection point
  • typically stored in a raw (sometimes binary) format
  • storage cost is cheap
  • accessing data means downloading files or most likely requires custom XINA tools
  • may be divided into multiple data sources (see below)
• Viewable Data
  • extracted from telemetry into XINA database(s)
  • telemetry is the single source of truth for this data, not intended to be user editable
    • (except under controlled circumstances with model-aware API calls)
  • add data is either mnemonic, instant, or interval (see below)
  • can be accessed and analyzed with built-in XINA tools
  • storage is expensive
  • optimizations may be needing depending on project requirements, data volumes
• User Metadata
  • additional data added by users, often directly through the XINA interface
  • XINA likely the primary repoository for this data
  • for example, a journal
• Definitions / References
  • may be user entered or defined outside XINA
  • may exist at model level or above (category/project level)
  • more formal and restricted than user metadata